Provenance management has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow enviro...
Artem Chebotko, Xubo Fei, Cui Lin, Shiyong Lu, Far...
A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workf...
Data lineage and data provenance are key to the management of scientific data. Not knowing the exact provenance and processing pipeline used to produce a derived data set often re...
Scientific workflow systems are increasingly used to automate complex data analyses, largely due to their benefits over traditional approaches for workflow design, optimization, a...
Data management is growing in complexity as largescale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage cap...